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The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm. 

This paper describes RF-based detection of un-manned aerial systems (UAS) without using transmit signals. The proposed scheme uses a sensitive magnetometer and digital signal processing algorithm to enable robust detection of high-torque/weig h t ratio rare earth magnet-based electric motors that are the enabling technology in electrical UAS. Preliminary experimental results with a 1 m^2 receive coil show reliable detection of a small UAS at distances of several meters. 

Abstract: Communication systems of the future will require hundreds of independent spatial channels achieved through dense antenna arrays connected to digital signal processing software defined radios. The cost and complexity of data converters are a significant concern with systems having hundreds of antennas. This paper explores frequency division multiplexing as an approach for augmenting the baseband signals of multiple antenna channels such that a single ADC can sample a multitude of antennas in an array. The approach is equally applicable to both massive MIMO and mm-wave digital wireless arrays. An example design based on Xilinx RF SoC for combining 4 antenna channels at 28 GHz into a single ADC is provided.